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% nomenclatureentry{aMLA@[{MLA}]\begingroup Modern Language Association\nomeqref {1.0}|nompageref}{1} \\


\nomenclature{Adjacency matrix}{A representation of the network in matrix form.}
% \nomenclature{Affiliation networks}{See two-mode networks.}
% \nomenclature{Balance}{A triadic effects theory bla..}
% \nomenclature{Bipartite}{See two-mode networks.}

\nomenclature{Anatomical vs Functional networks}{Two terms adopted from the field of neural networks, the anatomical network consists of a set of entities and all the physical connections between them (such as interconnections of nerve fibres in the brain.) Functional networks are sub-networks of the anatomical one, consisting of the connections between entities that are activated for the accomplishment of specific tasks.}

%\nomenclature{Private vs Broadcast Emails}{Broadcast emails are sent to multiple recipients and are found to cont}

\nomenclature{Balance}{A set of theories claiming that individuals strive for a state of coherence between the cognitive and affective states they ascribe to their various relationships. Insofar as a relationship reflects and constitutes a part of the identity of an individual, the theory states that individuals manage their relationships in a manner that increases their sense of consonance and decreases the dissonance between these parts. }

\nomenclature{Degree}{Used in one of two meanings: the degrees of separation between two nodes refers to the number of nodes spanned by the shortest path connecting the two. The degree of a node in a network refers to the number of relational ties associated with that node. Consequently, the degree distribution is the distribution of the number of relational ties associated with each node in the network. In directed networks, a distinction is made between the \textit{in-degree} of a focal node and its \textit{out-degree}, referring to the number of incoming or outgoing directed-ties incident to the focal node.}

\nomenclature{Density}{The proportion of connected \textit{dyads} to the total number of \textit{dyads} in a the network. A \textit{complete graph} has a density of $1$, and a  graph with no ties has a density of $0$.}

\nomenclature{DMTD}{Digitally Mediated Transactions Datasets, often referred to as \textbf{\textit{Big Data}}. These datasets are many orders of magnitude larger than \textit{TND}, exhibiting high temporal and spatial resolution of the data \citep{borge2013}. Compare with \textit{TND}.}


\nomenclature{Dyad}{Any unordered pair of nodes in the network. Each network of $n \ge 2$ nodes has exactly $\slantfrac{n\left( n-1 \right) }{2}$ dyads.}


\nomenclature{ERGM}{Exponential Random Graph Models - a class of statistical models that account for the presence (and absence) of relational ties in terms of local tie based structures, such as reciprocated ties, \textit{degree} heterogeneity and local transitivity.}

\nomenclature{Functionalism}{A theoretical doctrine according to which social phenomena (at the macro-level) exist by virtue of their features, whose function it is to sustain the group, protecting its integrity from internal or external threats. Compare with \textit{Path-Dependence}.}


% \nomenclature{Endogenous vs Exogenous}{Endogenous network features can be explained through the evolution of the network structure itself, for example skewed \textit{degree distribution} or \textit{transitivity} are endogenous features. In contrast, exogenous features are determined a-priori, examples include the number and identity of individuals in the network.}

% \nomenclature{ICT}{Information and Communication Technology.}

\nomenclature{Graph}{A mathematical representation of a network, $G(N,E)$ with a set of nodes $N=\left\lbrace 1,2,..., n  \right\rbrace$ and a set of edges $E=\left\lbrace \left\lbrace i_1,j_1  \right\rbrace, \left\lbrace i_2,j_2  \right\rbrace,...  \right\rbrace$, each of this set's members describing  an association between two nodes in the set $N$. A complete graph is one in which every \textit{dyad} is connected.}

\nomenclature{Homophily}{A feature of social networks, whereby connected individuals are likely to share similar traits, also known as \textit{assortative mixing}. Two mechanisms explain this feature; associated individuals can develop similar traits (\textit{Influence}), or similar traits of individuals can bring them to associate with each other (\textit{Selection}.) }


\nomenclature{Macro-Micro link}{Two different links between the macro and the micro are discussed in this work. A \textit{\textbf{definitional}} link refers to macro properties defined in terms of the micro (also known as constitutive, supervenience, analytical or aggregational link.) A \textit{\textbf{contingent}} link refers to macro properties that are influenced but not logically defined by micro-properties (also known as empirical, synthetic or causal link.) }

\nomenclature{Network events}{Consist of tie formation, tie dissolution and the changing properties of individual nodes.}

\nomenclature{Power distribution}{A distribuiton that follows a power law $\Pr\left( X > x \right) \sim x^{-\left( \alpha+1\right) } $ where $\alpha \gt 0$. The distribution is positively skewed and popularly known for its \textit{fat tail}, a tail much fatter than that of the normal distribution. Closely associated with the \textit{Lotka's law},  \textit{scale free}, \textit{Pareto}  and the \textit{Zipf} distributions.}

\nomenclature{Path}{A sequence of distinct nodes and ties, in which each node is incident with the ties following and preceding it in the sequence. }

\nomenclature{Path-Dependence}{A theoretical doctrine according to which social phenomena (at the macro-level) exist by virtue of a particular sequence of historical events, contingent occurrences that were not necessarily determined on the basis of prior historical conditions alone \citep{mahoney2000}. Compare with \textit{Functionalism}.}

\nomenclature{Popularity}{Well connected individuals `attract' more new ties than less connected ones, an effect in the network that is responsible to a considerable heterogeneity in the \textit{degree} distribution. %The popularity effect is linked to the existence of so called \textit{hubs}, nodes with an anomaliously large number of connections. 
\textit{Popularity} is closely related to \textit{accumulated advantage}, \textit{preferential attachment} or the well known \textit{Matthew effect} \citep{merton1968}.}


\nomenclature{QAP}{Quadratic Assignment Procedures, a bootstrapping approach to allow statistical inferences by comparing different networks among the same set of nodes.}

%\nomenclature{Relational tie}{Any association between two entities within a network can be considered a relational tie. If the tie is associated with a direction, it is known as a \textit{directed tie} or an \textit{arc}. If it is weighted, it is known as a \textit{weighted tie}. A social tie is a durable association that may involves expectations, obligations, trust and commitment, emotions and cognitive associations that may constrain individuals or serve as a resource known as \textit{social capital} }

\nomenclature{Reciprocity}{A feature in a network by which connected nodes reciprocate favours or exchange information in both directions. The reciprocity of a network could be measured either by calculating the proportion of reciprocating dyads or by using \textit{QAP} between the network's \textit{adjacency matrix }and its inverse.  Closely related to \textit{symmetry} and \textit{mutuality}. }

\nomenclature{Supervenience}{The set of properties $A$ supervenes on a set of properties in $B$ if there cannot be an $A$ difference without a $B$ difference. When the macro is uniquely defined by the distribution of a set of micro states, one speaks of the macro supervening over the micro.}

% \nomenclature{SQL}{Structured Query Language - a programming language designed for querying and updating data held in a relational database management systems.}


% \nomenclature{Symmetry}{Same as reciprocity.}

\nomenclature{Tie-Interdependency}{The notion that a relational tie has an effect on other ties in its vicinity. Examples include \textit{popularity effects}, \textit{homophily} and \textit{triadic effects}.}

\nomenclature{TND}{Traditional Network Data, network datasets commonly elicited by survey and questionnaire methoda \citep{marsden2011}. Compare with \textit{DMTD}.}

\nomenclature{Transitivity}{A feature of the network expressed by the adage `friends of my friends are my friends.' In a network with high transitivity, any two nodes sharing common contacts tend to be associated directly. High transitivity is sometimes associated with social capital, collaboration and a sense of equality, whereas low transitivity can be associated with hierarchy and inequality. Closely related to \textit{closure}, \textit{triangulation}, \textit{clustering} or \textit{balance}. The level of transitivity can be measured by the use of clustering coefficients or through \textit{ERGM}.}

\nomenclature{Triad}{Any unordered set of three nodes in the network. Each network of $n \ge 3$ nodes has exactly $\slantfrac{n\left( n-1 \right)\left( n-2 \right) }{6}$ triads. Triads may be connected or disconnected. A connected triad (with at least two ties) is known as a \textit{triplet}.}


\nomenclature{Triadic effects}{A set of network effects which involve three nodes. Examples include \textit{transitivity} or \textit{balance}.}

\nomenclature{Two-mode network}{A network that involves two types of nodes, members of one type directly affiliated with members of the other type but not with one another. Examples include actors affiliated with films, individuals with social events, or directors with boards of companies. Also known as affiliation or bipartite networks.}






